Research on classification method of wavelet entropy and Fuzzy Neural Networks for motor imagery EEG

Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics [1]. Here, new method based on wavelet entropy (WE) and Fuzzy Neural Networks (FNN) is applied. The WE carries information about the degree of order/disorder associated with a multi-frequency signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals. This paper has tried to use fuzzy neural networks (FNN) as a classification method, which combines fuzzy membership and neural network frame, to drive the model automatically update in training and leaning processes, thus it can be applied in the simulations of complex conditions. In this way, EEG signals under motor imagery conditions were analyzed.

[1]  Haralambos Sarimveis,et al.  A classification technique based on radial basis function neural networks , 2006, Adv. Eng. Softw..

[2]  E. Basar,et al.  Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance , 2002, Journal of Neuroscience Methods.

[3]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[4]  Mustafa Poyraz,et al.  Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction , 2009, Expert Syst. Appl..

[5]  Qian Qing-quan,et al.  Study of a new method for power system transients classification based on wavelet entropy and neural network , 2011 .

[6]  Masao Mukaidono,et al.  A fuzzy neural network for pattern classification and feature selection , 2002, Fuzzy Sets Syst..

[7]  Gengdai Liu,et al.  A reactive and protective character motion generation algorithm , 2010, Int. J. Comput. Appl. Technol..

[8]  Albert T. Jones,et al.  Using neural networks to monitor supply chain behaviour , 2011, Int. J. Comput. Appl. Technol..

[9]  Sami Ekici,et al.  Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition , 2008, Expert Syst. Appl..

[10]  O. A. Rosso,et al.  EEG analysis using wavelet-based information tools , 2006, Journal of Neuroscience Methods.

[11]  Mohamed Faten Zhani,et al.  Analysis of prediction performance of training-based models using real network traffic , 2008, 2008 International Symposium on Performance Evaluation of Computer and Telecommunication Systems.

[12]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[13]  Khaled Daqrouq,et al.  Wavelet entropy and neural network for text-independent speaker identification , 2011, Eng. Appl. Artif. Intell..

[14]  Engin Avci,et al.  Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system , 2008, Appl. Soft Comput..

[15]  David Labat,et al.  Recent advances in wavelet analyses: Part 1. A review of concepts , 2005 .

[16]  Zhenbao Liu,et al.  A 3D shape classifier with neural network supervision , 2010, Int. J. Comput. Appl. Technol..

[17]  Chia-Feng Juang,et al.  Recurrent type-2 fuzzy neural network using Haar wavelet energy and entropy features for speech detection in noisy environments , 2012, Expert Syst. Appl..

[18]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[19]  Hasan Ocak,et al.  Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy , 2009, Expert Syst. Appl..

[20]  S. Kar,et al.  EEG signal analysis for the assessment and quantification of driver’s fatigue , 2010 .